ZTaint-Havoc: From Havoc Mode to Zero-Execution Fuzzing-Driven Taint Inference

Yuchong Xie, Wenhui Zhang, Dongdong She

Abstract: Fuzzing is a popular software testing technique for discovering vulnerabilities. A central problem in fuzzing is identifying hot bytes that can influence program behavior. Taint analysis can track the data flow of hot bytes in a white-box fashion, but it often suffers from stability issues and cannot run on large real-world programs. Fuzzing-Driven Taint Inference (FTI) is a simple black-box technique to track hot bytes for fuzzing. It monitors the dynamic program behaviors of program execution instances and further infers hot bytes in a black-box fashion. However, this method requires additional O(N) program executions and incurs a large runtime overhead.We observe that a widely used mutation scheme in fuzzing--havoc mode can be adapted into a lightweight FTI with zero additional program execution. In this work, we first present a computational model of the havoc mode that formally describes its mutation process. Based on this model, we show that the havoc mode can simultaneously launch FTI while generating and executing new testcases. Further, we propose a novel FTI called ZTaint-Havoc that doesn't need any additional program execution. ZTaint-Havoc incurs minimal instrumentation overhead of 3.84% on UniBench and 12.58% on FuzzBench, respectively. In the end, we give an effective mutation algorithm using the hot bytes identified by ZTaint-Havoc.We conduct a comprehensive evaluation to investigate the computational model of havoc mode. Our evaluation result justifies that it is feasible to adapt the havoc mode to an efficient FTI without any additional program execution. We further implement our approach as a prototype ZTaint-Havoc based on the havoc mode of AFL++. We evaluate ZTaint-Havoc on two fuzzing datasets FuzzBench and UniBench. Our extensive evaluation results show that ZTaint-Havoc improves edge coverage by up to 33.71% on FuzzBench and 51.12% on UniBench over vanilla AFL++, with average improvements of 2.97% and 6.12% respectively, in 24-hour campaigns.